Dynamic valuation system using object relationships and composite object data

ABSTRACT

The disclosed embodiments relate to systems and methods for generating an optimal solution for determining a value for one or more base data objects. A plurality of solutions include one or more composite data objects transacted by a transaction system processor. The composite data objects include the one or more base data objects. An optimal solution is generated by using data indicative of a level of activity and a number of sources for each of the plurality of composite data objects.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation under 37 C.F.R. § 1.53(b) of U.S.patent application Ser. No. 15/192,203 filed Jun. 24, 2016 now U.S. Pat.No. ______, the entire disclosure of which is incorporated by referencein its entirety and relied upon.

BACKGROUND

Computer processing speeds depend in large part on the amount of databeing processed and the complexity of the operations and processingbeing performed on the data. Some computing systems include many, e.g.,hundreds or thousands, of objects of differing types, and attempt tocompute values for the objects. Some of the objects may be related orbased on other objects, and the system environment may impose rules andrestrictions on the objects. In a complex environment, values for theobjects may be computed using the rules and restrictions using differentsolutions. Many times the optimal solution for processing the values maybe indeterminate. A computer tasked with calculating values andoptimizing values based on the rules and restrictions may follow a setof procedures, routines or sub-routines to arrive at the final values.The set of procedures, routines or sub-routines may change over time asthe values of the underlying objects change. For computers handlingmultiple inter-related objects having changing rules and restrictions,it is a challenge to efficiently process and compute final values forthe objects. Identifying a specific solution for calculating the finalvalue can decrease redundancy and can increase processing efficiency.

Accordingly, there is a need for systems and methods that can generateand select optimal solutions for calculating values for inter-relatedobjects in an efficient and timely manner, so that the optimized objectsolutions justify any increase in processing time due to theoptimization.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 depicts an illustrative computer network system that may be usedto implement aspects of the disclosed embodiments.

FIG. 2 depicts an illustrative embodiment of a general computer systemfor use with the disclosed embodiments.

FIG. 3A depicts an illustrative embodiment of an optimization module ofthe computer network system of FIG. 1.

FIG. 3B depicts an illustrative example of base objects and compositeobjects stored in a memory of the optimization module of FIG. 3A.

FIG. 4 depicts an example graph of base objects and composite objects.

FIG. 5 depicts an example simple pricing complex.

FIG. 6 depicts an example flowchart indicating a method of implementingthe disclosed optimization system.

FIG. 7 depicts an example rated graph.

FIG. 8 depicts an example flowchart indicating a method of implementingthe disclosed optimization system using a maximum spanning tree.

FIG. 9 depicts an example product complex including multiple coreproducts.

FIG. 10A and FIG. 10B depict a maximum spanning tree for an example subgroup of the product complex of FIG. 9.

FIG. 11 depicts an example maximum spanning graph.

FIG. 12A and FIG. 12B depict a graph of and the maximum spanning treefor an example second sub-group of the product complex of FIG. 9.

DETAILED DESCRIPTION

The disclosed embodiments relate generally to generating and selectingoptimum solutions comprising related composite objects for calculatingvalues for base objects. The value of a base object may be calculated byselecting one or more related composite objects in order to link to acore object that has a known value. The veracity and/or reliability ofthe value of the related composite objects and core object, asaccurately representing the actual value thereof, may be corroboratedusing external data sources. Specifically, the disclosed embodimentsoptimize overall system performance by predictably and efficientlydetermining and selecting solutions from numerous different possiblecombinations that allow for estimating a value with a high level ofconfidence as to the accuracy thereof.

Thus, the disclosed embodiments reduce the load on a computer byidentifying, prior to computing the desired value, a subset ofsolutions, from among the set of all possible solutions, that includethe highest confidence values for related composite objects andeliminating those other solutions such that the computer need onlyresolve the optimal subset of solutions to compute the desired value. Inother words, the disclosed embodiments rely on desirable objectrelationships to identify a solution for calculating object values. Thedesired object relationships may be derived from data input into thesystem. In one embodiment, the desired object relationships reflect ahigh level of user interest.

The disclosed embodiments may be implemented so as to exclude solutionsthat include relationships that are not sufficiently corroborated sothat the object values may only be derived from high quality inputs. Inone embodiment, the object relationships are ranked or are hierarchicalin nature, and the computing systems begins with the highest rankedrelationship, then iteratively process the second-highest rankedrelationship, and so on. Instead of calculating values for each possiblecombination, the relationships are used to cull undesirable solutions sothat the optimum solution can be identified quickly.

Objects may be implemented in code, using, among other things, atangible computer-readable medium comprising computer-executableinstructions (e.g., executable software code). Alternatively, objectsmay be implemented as software code, firmware code, specificallyconfigured hardware or processors, and/or a combination of theaforementioned. An object may be implemented and stored as a set ofrelated data, e.g., a database. Objects may be implemented using apre-defined data structure. An object may be implemented as an instanceof a class that contains data and methods for processing the data. Forexample, an object may be a self-contained entity that includes data andprocedures to manipulate the data. An object may be any item in thecomputing environment that can be individually manipulated, selected orprocessed. Objects may be exposed as shapes, pictures or words in adisplay screen or in a user interface.

One exemplary environment where optimizing computer processing ofsolutions is desirable is in financial markets, and in particular,electronic financial exchanges, such as a futures exchange, such as theChicago Mercantile Exchange Inc. (CME). In particular, an exchange mayoffer multiple products and contracts for purchase that may berepresented as objects in the computing system. The associated costs andvalues of objects may be considered to be related data sets. An exchangecomputer system may also be constrained by the tradable positions ofmarkets, such as for example, bid and ask values for the differentcontracts, available on the exchange.

A financial instrument trading system, such as a futures exchange, suchas the Chicago Mercantile Exchange Inc. (CME), provides a contractmarket where financial instruments, e.g., futures and options onfutures, are traded using electronic systems. “Futures” is a term usedto designate all contracts for the purchase or sale of financialinstruments or physical commodities for future delivery or cashsettlement on a commodity futures exchange. A futures contract is alegally binding agreement to buy or sell a commodity at a specifiedprice at a predetermined future time. An option contract is the right,but not the obligation, to sell or buy the underlying instrument (inthis case, a futures contract) at a specified price within a specifiedtime. The commodity to be delivered in fulfillment of the contract, oralternatively the commodity for which the cash market price shalldetermine the final settlement price of the futures contract, is knownas the contract's underlying reference or “underlier.” The terms andconditions of each futures contract are standardized as to thespecification of the contract's underlying reference commodity, thequality of such commodity, quantity, delivery date, and means ofcontract settlement. Cash settlement is a method of settling a futurescontract whereby the parties effect final settlement when the contractexpires by paying/receiving the loss/gain related to the contract incash, rather than by effecting physical sale and purchase of theunderlying reference commodity at a price determined by the futurescontract, price. Options and futures may be based on more generalizedmarket indicators, such as stock indices, interest rates, futurescontracts and other derivatives.

An exchange may provide for a centralized “clearing house” through whichtrades made must be confirmed, matched, and settled each day untiloffset or delivered. The clearing house may be an adjunct to anexchange, and may be an operating division of an exchange, which isresponsible for settling trading accounts, clearing trades, collectingand maintaining performance bond funds, regulating delivery, andreporting trading data. One of the roles of the clearing house is tomitigate credit risk. Clearing is the procedure through which theclearing house becomes buyer to each seller of a futures contract, andseller to each buyer, also referred to as a novation, and assumesresponsibility for protecting buyers and sellers from financial loss dueto breach of contract, by assuring performance on each contract. Aclearing member is a firm qualified to clear trades through the clearinghouse.

The clearing house of an exchange clears, settles and guarantees matchedtransactions in contracts occurring through the facilities of theexchange. In addition, the clearing house establishes and monitorsfinancial requirements for clearing members and conveys certain clearingprivileges in conjunction with the relevant exchange markets.

The clearing house establishes clearing level performance bonds(margins) for all products of the exchange and establishes minimumperformance bond requirements for customers of such products. Aperformance bond, also referred to as a margin requirement, correspondswith the funds that must be deposited by a customer with his or herbroker, by a broker with a clearing member or by a clearing member withthe clearing house, for the purpose of insuring the broker or clearinghouse against loss on open futures or options contracts. This is not apart payment on a purchase. The performance bond helps to ensure thefinancial integrity of brokers, clearing members and the exchange as awhole. The performance bond refers to the minimum dollar depositrequired by the clearing house from clearing members in accordance withtheir positions. Maintenance, or maintenance margin, refers to a sum,usually smaller than the initial performance bond, which must remain ondeposit in the customer's account for any position at all times. Theinitial margin is the total amount of margin per contract required bythe broker when a futures position is opened. A drop in funds below thislevel requires a deposit back to the initial margin levels, i.e., aperformance bond call. If a customer's equity in any futures positiondrops to or under the maintenance level because of adverse price action,the broker must issue a performance bond/margin call to restore thecustomer's equity. A performance bond call, also referred to as a margincall, is a demand for additional funds to bring the customer's accountback up to the initial performance bond level whenever adverse pricemovements cause the account to go below the maintenance.

The exchange derives its financial stability in large part by removingdebt obligations among market participants as they occur. This isaccomplished by determining a settlement price at the close of themarket each day for each contract and marking all open positions to thatprice, referred to as “mark to market.” Every contract is debited orcredited based on that trading session's gains or losses. As prices movefor or against a position, funds flow into and out of the tradingaccount. In the case of the CME, each business day by 6:40 a.m. Chicagotime, based on the mark-to-the-market of all open positions to theprevious trading day's settlement price, the clearing house pays to orcollects cash from each clearing member. This cash flow, known assettlement variation, is performed by CME's settlement banks based oninstructions issued by the clearing house. All payments to andcollections from clearing members are made in “same-day” funds. Inaddition to the 6:40 a.m. settlement, a daily intra-day mark-to-themarket of all open positions, including trades executed during theovernight GLOBEX®, the CME's electronic trading systems, trading sessionand the current day's trades matched before 11:15 a.m., is performedusing current prices. The resulting cash payments are made intra-day forsame day value. In times of extreme price volatility, the clearing househas the authority to perform additional intra-day mark-to-the-marketcalculations on open positions and to call for immediate payment ofsettlement variation. CME's mark-to-the-market settlement system differsfrom the settlement systems implemented by many other financial markets,including the interbank, Treasury securities, over-the-counter foreignexchange and debt, options, and equities markets, where participantsregularly assume credit exposure to each other. In those markets, thefailure of one participant can have a ripple effect on the solvency ofthe other participants. Conversely, CME's mark-to-the-market system doesnot allow losses to accumulate over time or allow a market participantthe opportunity to defer losses associated with market positions.

In order to minimize risk to the exchange while minimizing the burden onmembers, it is desirable to approximate the requisite performance bondor margin requirement as closely as possible to the actual positions ofthe account at any given time. An exchange may use a settlement methodto determine the position of a contract. With some objects or spreadinstruments, the market is sufficiently inactive during or at the end ofthe trading day. Very little, if any, trades may occur during a givenday. In such cases, because of low liquidity and trading activity, itmay be difficult to determine daily settlement prices for purposes ofaccurately estimating performance bond requirements. In many cases, thelogic used to determine a settlement price for a contract could resultin multiple viable or possible settlement prices. The logic used toestimate settlement prices may be based on the prices for relatedobjects. Many of the objects offered are related each other withoutright/spread relationships. These relationships creates numerousdifferent possible settlement solutions. Prices of the objects may bederived from the outright or spread relationships.

While the disclosed embodiments may be discussed in relation to futuresand/or options on futures trading, it should be appreciated that thedisclosed embodiments may be applicable to any equity, fixed incomesecurity, currency, commodity, options or futures trading system ormarket now available or later developed. It should be appreciated that atrading environment, such as a futures exchange as described herein,implements one or more economic markets where rights and obligations maybe traded. As such, a trading environment may be characterized by a needto maintain market integrity, transparency, predictability,fair/equitable access and participant expectations with respect thereto.For example, an exchange must respond to inputs, such as trader orders,cancelations, etc., in a manner as expected by the market participants,such as based on market data, e.g., prices, available counter-orders,etc., to provide an expected level of certainty that transactions willoccur in a consistent and predictable manner and without unknown orunascertainable risks. In addition, it should be appreciated thatelectronic trading systems further impose additional expectations anddemands by market participants as to transaction processing speed,latency, capacity and response time, while creating additionalcomplexities relating thereto. Accordingly, as will be described, thedisclosed embodiments may further include functionality to ensure thatthe expectations of market participants are met, e.g., thattransactional integrity and predictable system responses are maintained.

As was discussed above, electronic trading systems ideally attempt tooffer an efficient, fair and balanced market where market prices reflecta true consensus of the value of products traded among the marketparticipants, where the intentional or unintentional influence of anyone market participant is minimized if not eliminated, and where unfairor inequitable advantages with respect to information access areminimized if not eliminated.

Although described below in connection with examples involvinginstruments having multiple components, such as calendar and butterflyspread instruments, the methods described herein are well suited fordetermining final values for any variety of objects conforming to a setof rules or relationships, such as for example, determining settlementprices for a variety of instruments based on a related market.

Generally, the disclosed embodiments may be applicable to any computerprocessing system that is constrained by a variety of rules and datavalues. When a computer processor attempts to compute a large number ofdata sets in an environment including rules constraints and dataconstraints, the number of possible solutions or combinations of valuescan become unwieldy.

The disclosed embodiments may be applicable to contracts for any type ofunderlier, commodity, equity, option, or futures trading system ormarket now available or later developed. The disclosed embodiments arealso not limited to intra-market spread instruments, and accordingly mayalso be used in connection with inter-market spread instruments forcontracts associated with different commodities.

While the disclosed embodiments may be described in reference to theCME, it should be appreciated that these embodiments are applicable toany exchange. Such other exchanges may include a clearing house that,like the CME clearing house, clears, settles and guarantees all matchedtransactions in contracts of the exchange occurring through itsfacilities. In addition, such clearing houses establish and monitorfinancial requirements for clearing members and convey certain clearingprivileges in conjunction with the relevant exchange markets.

The disclosed embodiments are also not limited to uses by a clearinghouse or exchange for purposes of enforcing a performance bond or marginrequirement. For example, a market participant may use the disclosedembodiments in a simulation or other analysis of a portfolio. In suchcases, the settlement price may be useful as an indication of a value atrisk and/or cash flow obligation rather than a performance bond. Thedisclosed embodiments may also be used by market participants or otherentities to forecast or predict the effects of a prospective position onthe margin requirement of the market participant.

The methods and systems described herein may be integrated or otherwisecombined with various risk management methods and systems, such as therisk management methods and systems described in U.S. Pat. No. 7,769,667entitled “System and Method for Activity Based Margining” (the '667patent”), the entire disclosure of which is incorporated by referenceherein and relied upon. For example, the methods and systems describedherein may be configured as a component or module of the risk managementsystems described in the above-referenced patent. Alternatively oradditionally, the disclosed methods may generate data to be provided tothe systems described in the above-referenced patent. For example, thesettlement prices determined by the disclosed embodiments may beincorporated into margin requirement(s) determined by the riskmanagement method or system.

In one embodiment, the disclosed methods and systems are integrated orotherwise combined with the risk management system implemented by CMEcalled Standard Portfolio Analysis of Risk™ (SPAN®). The SPAN systembases performance bond requirements on the overall risk of theportfolios using parameters as determined by CME's Board of Directors,and thus represents a significant improvement over other performancebond systems, most notably those that are “strategy-based” or“delta-based.” Further details regarding SPAN are set forth in the '667patent.

In one embodiment, the disclosed embodiments may be integrated orcombined with a margin model, such as a margin model different fromSPAN. For example, a margin model may be implemented to generatemultiple settlement prices.

The embodiments may be described in terms of a distributed computingsystem. The particular examples identify a specific set of componentsuseful in a futures and options exchange. However, many of thecomponents and inventive features are readily adapted to otherelectronic trading environments. The specific examples described hereinmay teach specific protocols and/or interfaces, although it should beunderstood that the principles involved may be extended to, or appliedin, other protocols and interfaces.

It should be appreciated that the plurality of entities utilizing orinvolved with the disclosed embodiments, e.g., the market participants,may be referred to by other nomenclature reflecting the role that theparticular entity is performing with respect to the disclosedembodiments and that a given entity may perform more than one roledepending upon the implementation and the nature of the particulartransaction being undertaken, as well as the entity's contractual and/orlegal relationship with another market participant and/or the exchange.

An exemplary trading network environment for implementing tradingsystems and methods is shown in FIG. 1. An exchange computer system 100receives messages that include orders and transmits market data relatedto orders and trades to users, such as via wide area network 126 and/orlocal area network 124 and computer devices 114, 116, 118, 120 and 122,as will be described below, coupled with the exchange computer system100.

Herein, the phrase “coupled with” is defined to mean directly connectedto or indirectly connected through one or more intermediate components.Such intermediate components may include both hardware and softwarebased components. Further, to clarify the use in the pending claims andto hereby provide notice to the public, the phrases “at least one of<A>, <B>, . . . and <N>” or “at least one of <A>, <B>, . . . <N>, orcombinations thereof” are defined by the Applicant in the broadestsense, superseding any other implied definitions herebefore orhereinafter unless expressly asserted by the Applicant to the contrary,to mean one or more elements selected from the group comprising A, B, .. . and N, that is to say, any combination of one or more of theelements A, B, . . . or N including any one element alone or incombination with one or more of the other elements which may alsoinclude, in combination, additional elements not listed.

The exchange computer system 100 may be implemented with one or moremainframe, desktop or other computers, such as the example computer 200described below with respect to FIG. 2. A user database 102 may beprovided which includes information identifying traders and other usersof exchange computer system 100, such as account numbers or identifiers,user names and passwords. An account data module 104 may be providedwhich may process account information that may be used during trades.

A match engine module 106 may be included to match bid and offer pricesand may be implemented with software that executes one or morealgorithms for matching bids and offers. A trade database 108 may beincluded to store information identifying trades and descriptions oftrades. In particular, a trade database may store informationidentifying the time that a trade took place and the contract price. Anorder book module 110 may be included to compute or otherwise determinecurrent bid and offer prices, e.g., in a continuous auction market, oralso operate as an order accumulation buffer for a batch auction market.A market data module 112 may be included to collect market data andprepare the data for transmission to users.

A risk management module 134 may be included to compute and determine auser's risk utilization in relation to the user's defined riskthresholds. The risk management module 134 may also be configured todetermine risk assessments or exposure levels in connection withpositions held by a market participant.

The risk management module 134 may be configured to administer, manageor maintain one or more margining mechanisms implemented by the exchangecomputer system 100. Such administration, management or maintenance mayinclude managing a number of database records reflective of marginaccounts of the market participants. In some embodiments, the riskmanagement module 134 implements one or more aspects of the disclosedembodiments, including, for instance, principal component analysis (PCA)based margining, in connection with interest rate swap (IRS) portfolios,as described below.

An order processing module 136 may be included to decompose delta-based,spread instrument, bulk and other types of composite orders forprocessing by the order book module 110 and/or the match engine module106. The order processing module 136 may also be used to implement oneor more procedures related to clearing an order.

A settlement module 140 (or settlement processor or other paymentprocessor) may be included to provide one or more functions related tosettling or otherwise administering transactions cleared by theexchange. Settlement module 140 of the exchange computer system 100 mayimplement one or more settlement price determination techniques.Settlement-related functions need not be limited to actions or eventsoccurring at the end of a contract term. For instance, in someembodiments, settlement-related functions may include or involve dailyor other mark to market settlements for margining purposes. In somecases, the settlement module 140 may be configured to communicate withthe trade database 108 (or the memory(ies) on which the trade database108 is stored) and/or to determine a payment amount based on a spotprice, the price of the futures contract or other financial instrument,or other price data, at various times. The determination may be made atone or more points in time during the term of the financial instrumentin connection with a margining mechanism. For example, the settlementmodule 140 may be used to determine a mark to market amount on a dailybasis during the term of the financial instrument. Such determinationsmay also be made on a settlement date for the financial instrument forthe purposes of final settlement.

In some embodiments, the settlement module 140 may be integrated to anydesired extent with one or more of the other modules or processors ofthe exchange computer system 100. For example, the settlement module 140and the risk management module 134 may be integrated to any desiredextent. In some cases, one or more margining procedures or other aspectsof the margining mechanism(s) may be implemented by the settlementmodule 140.

An optimization module 142 may be included to generate and select bestor optimum solutions for determining the value of an object. Theoptimization module 142 may determine solutions by analyzing a pluralityof values for one or more composite objects in order to evaluaterelationships between one or more objects that are included in thecomposite objects. The optimization module 142 may be coupled with thesettlement module in order to generate a solution for the settlementmodule 140 to use for daily or other mark to market settlements formargining purposes. The optimization module 142 may store multiplesolutions for each object.

An optimum solution or solutions may be derived from a core object andthe combination(s) of objects that conform to a hierarchicalprioritization of object relationships. The optimization module 142 mayfor example be utilized in conjunction the trade database 108 toidentify user interest in one or more of the composite objects. Theoptimization module 142 may prioritize or rank each composite objectbased on user interest or other received data such as total revenue,number of users, open interest among others. The optimization module 142may generate, identify or otherwise derive a path from, i.e. a direct orindirect mathematical linkage or relationship between, a core object toone or more base objects. The path/relationship may include or otherwisebe composed of or defined by one or more composite objects that share acommon base object, i.e. that themselves feature a direct or indirectmathematical linkage or relationship there between. The path may includeor be specified by an ordered list of composite objects that may definea solution that the settlement module 140 may use for settlementpurposes.

It should be appreciated that concurrent processing limits may bedefined by or imposed separately or in combination, as was describedabove, on one or more of the trading system components, including theuser database 102, the account data module 104, the match engine module106, the trade database 108, the order book module 110, the market datamodule 112, the risk management module 134, the order processing module136, the settlement module 140, or other component of the exchangecomputer system 100.

One skilled in the art will appreciate that one or more modulesdescribed herein may be implemented using, among other things, atangible computer-readable medium comprising computer-executableinstructions (e.g., executable software code). Alternatively, modulesmay be implemented as software code, firmware code, specificallyconfigured hardware or processors, and/or a combination of theaforementioned. For example the modules may be embodied as part of anexchange 100 for financial instruments. It should be appreciated thedisclosed embodiments may be implemented as a different or separatemodule of the exchange computer system 100, or a separate computersystem coupled with the exchange computer system 100 so as to haveaccess to margin account record, pricing, and/or other data. Asdescribed above, the disclosed embodiments may be implemented as acentrally accessible system or as a distributed system, e.g., where someof the disclosed functions are performed by the computer systems of themarket participants.

The trading network environment shown in FIG. 1 includes exemplarycomputer devices 114, 116, 118, 120 and 122 which depict differentexemplary methods or media by which a computer device may be coupledwith the exchange computer system 100 or by which a user maycommunicate, e.g., send and receive, trade or other informationtherewith. It should be appreciated that the types of computer devicesdeployed by traders and the methods and media by which they communicatewith the exchange computer system 100 is implementation dependent andmay vary and that not all of the depicted computer devices and/ormeans/media of communication may be used and that other computer devicesand/or means/media of communications, now available or later developedmay be used. Each computer device, which may comprise a computer 200described in more detail below with respect to FIG. 2, may include acentral processor, specifically configured or otherwise, that controlsthe overall operation of the computer and a system bus that connects thecentral processor to one or more conventional components, such as anetwork card or modem. Each computer device may also include a varietyof interface units and drives for reading and writing data or files andcommunicating with other computer devices and with the exchange computersystem 100. Depending on the type of computer device, a user caninteract with the computer with a keyboard, pointing device, microphone,pen device or other input device now available or later developed.

An exemplary computer device 114 is shown directly connected to exchangecomputer system 100, such as via a T1 line, a common local area network(LAN) or other wired and/or wireless medium for connecting computerdevices, such as the network 220 shown in FIG. 2 and described belowwith respect thereto. The exemplary computer device 114 is further shownconnected to a radio 132. The user of radio 132, which may include acellular telephone, smart phone, or other wireless proprietary and/ornon-proprietary device, may be a trader or exchange employee. The radiouser may transmit orders or other information to the exemplary computerdevice 114 or a user thereof. The user of the exemplary computer device114, or the exemplary computer device 114 alone and/or autonomously, maythen transmit the trade or other information to the exchange computersystem 100.

Exemplary computer devices 116 and 118 are coupled with a local areanetwork (“LAN”) 124 which may be configured in one or more of thewell-known LAN topologies, e.g., star, daisy chain, etc., and may use avariety of different protocols, such as Ethernet, TCP/IP, etc. Theexemplary computer devices 116 and 118 may communicate with each otherand with other computer and other devices which are coupled with the LAN124. Computer and other devices may be coupled with the LAN 124 viatwisted pair wires, coaxial cable, fiber optics or other wired orwireless media. As shown in FIG. 1, an exemplary wireless personaldigital assistant device (“PDA”) 122, such as a mobile telephone, tabletbased compute device, or other wireless device, may communicate with theLAN 124 and/or the Internet 126 via radio waves, such as via Wi-Fi,Bluetooth and/or a cellular telephone based data communicationsprotocol. PDA 122 may also communicate with exchange computer system 100via a conventional wireless hub 128.

FIG. 1 also shows the LAN 124 coupled with a wide area network (“WAN”)126 which may be comprised of one or more public or private wired orwireless networks. In one embodiment, the WAN 126 includes the Internet126. The LAN 124 may include a router to connect LAN 124 to the Internet126. Exemplary computer device 120 is shown coupled directly to theInternet 126, such as via a modem, DSL line, satellite dish or any otherdevice for connecting a computer device to the Internet 126 via aservice provider therefore as is known. LAN 124 and/or WAN 126 may bethe same as the network 220 shown in FIG. 2 and described below withrespect thereto.

Users of the exchange computer system 100 may include one or more marketmakers 130 which may maintain a market by providing constant bid andoffer prices for a derivative or security to the exchange computersystem 100, such as via one of the exemplary computer devices depicted.The exchange computer system 100 may also exchange information withother match or trade engines, such as trade engine 138. One skilled inthe art will appreciate that numerous additional computers and systemsmay be coupled to exchange computer system 100. Such computers andsystems may include clearing, regulatory and fee systems.

The operations of computer devices and systems shown in FIG. 1 may becontrolled by computer-executable instructions stored on anon-transitory computer-readable medium. For example, the exemplarycomputer device 116 may store computer-executable instructions forreceiving order information from a user, transmitting that orderinformation to exchange computer system 100 in electronic messages,extracting the order information from the electronic messages, executingactions relating to the messages, and/or calculating values fromcharacteristics of the extracted order to facilitate matching orders andexecuting trades. In another example, the exemplary computer device 118may include computer-executable instructions for receiving market datafrom exchange computer system 100 and displaying that information to auser. In another example, the exemplary computer device 118 may includea non-transitory computer-readable medium that stores instructions forpredicting and/or publishing a current response time or current matchengine latency as described herein.

Numerous additional servers, computers, handheld devices, personaldigital assistants, telephones and other devices may also be connectedto exchange computer system 100. Moreover, one skilled in the art willappreciate that the topology shown in FIG. 1 is merely an example andthat the components shown in FIG. 1 may include other components notshown and be connected by numerous alternative topologies.

Referring to FIG. 2, an illustrative embodiment of a general computersystem 200 is shown. The computer system 200 can include a set ofinstructions that can be executed to cause the computer system 200 toperform any one or more of the methods or computer based functionsdisclosed herein. The computer system 200 may operate as a standalonedevice or may be connected, e.g., using a network, to other computersystems or peripheral devices. Any of the components discussed above,such as the processor 202, may be a computer system 200 or a componentin the computer system 200. The computer system 200 may be specificallyconfigured to implement a match engine, margin processing, payment orclearing function on behalf of an exchange, such as the ChicagoMercantile Exchange, of which the disclosed embodiments are a componentthereof.

In a networked deployment, the computer system 200 may operate in thecapacity of a server or as a client user computer in a client-serveruser network environment, or as a peer computer system in a peer-to-peer(or distributed) network environment. The computer system 200 can alsobe implemented as or incorporated into various devices, such as apersonal computer (PC), a tablet PC, a set-top box (STB), a personaldigital assistant (PDA), a mobile device, a palmtop computer, a laptopcomputer, a desktop computer, a communications device, a wirelesstelephone, a land-line telephone, a control system, a camera, a scanner,a facsimile machine, a printer, a pager, a personal trusted device, aweb appliance, a network router, switch or bridge, or any other machinecapable of executing a set of instructions (sequential or otherwise)that specify actions to be taken by that machine. In a particularembodiment, the computer system 200 can be implemented using electronicdevices that provide voice, video or data communication. Further, whilea single computer system 200 is illustrated, the term “system” shallalso be taken to include any collection of systems or sub-systems thatindividually or jointly execute a set, or multiple sets, of instructionsto perform one or more computer functions.

As illustrated in FIG. 2, the computer system 200 may include aprocessor 202, e.g., a central processing unit (CPU), a graphicsprocessing unit (GPU), or both. The processor 202 may be a component ina variety of systems. For example, the processor 202 may be part of astandard personal computer or a workstation. The processor 202 may beone or more general processors, digital signal processors, specificallyconfigured processors, application specific integrated circuits, fieldprogrammable gate arrays, servers, networks, digital circuits, analogcircuits, combinations thereof, or other now known or later developeddevices for analyzing and processing data. The processor 202 mayimplement a software program, such as code generated manually (i.e.,programmed).

The computer system 200 may include a memory 204 that can communicatevia a bus 208. The memory 204 may be a main memory, a static memory, ora dynamic memory. The memory 204 may include, but is not limited to,computer readable storage media such as various types of volatile andnon-volatile storage media, including but not limited to random accessmemory, read-only memory, programmable read-only memory, electricallyprogrammable read-only memory, electrically erasable read-only memory,flash memory, magnetic tape or disk, optical media and the like. In oneembodiment, the memory 204 includes a cache or random access memory forthe processor 202. In alternative embodiments, the memory 204 isseparate from the processor 202, such as a cache memory of a processor,the system memory, or other memory. The memory 204 may be an externalstorage device or database for storing data. Examples include a harddrive, compact disc (“CD”), digital video disc (“DVD”), memory card,memory stick, floppy disc, universal serial bus (“USB”) memory device,or any other device operative to store data. The memory 204 is operableto store instructions executable by the processor 202. The functions,acts or tasks illustrated in the figures or described herein may beperformed by the programmed processor 202 executing the instructions 212stored in the memory 204. The functions, acts or tasks are independentof the particular type of instructions set, storage media, processor orprocessing strategy and may be performed by software, hardware,integrated circuits, firmware, micro-code and the like, operating aloneor in combination. Likewise, processing strategies may includemultiprocessing, multitasking, parallel processing and the like.

As shown, the computer system 200 may further include a display unit214, such as a liquid crystal display (LCD), an organic light emittingdiode (OLED), a flat panel display, a solid state display, a cathode raytube (CRT), a projector, a printer or other now known or later developeddisplay device for outputting determined information. The display 214may act as an interface for the user to see the functioning of theprocessor 202, or specifically as an interface with the software storedin the memory 204 or in the drive unit 206.

Additionally, the computer system 200 may include an input device 216configured to allow a user to interact with any of the components ofsystem 200. The input device 216 may be a number pad, a keyboard, or acursor control device, such as a mouse, or a joystick, touch screendisplay, remote control or any other device operative to interact withthe system 200.

In a particular embodiment, as depicted in FIG. 2, the computer system200 may also include a disk or optical drive unit 206. The disk driveunit 206 may include a computer-readable medium 210 in which one or moresets of instructions 212, e.g., software, can be embedded. Further, theinstructions 212 may embody one or more of the methods or logic asdescribed herein. In a particular embodiment, the instructions 212 mayreside completely, or at least partially, within the memory 204 and/orwithin the processor 202 during execution by the computer system 200.The memory 204 and the processor 202 also may include computer-readablemedia as discussed above.

The present disclosure contemplates a computer-readable medium thatincludes instructions 212 or receives and executes instructions 212responsive to a propagated signal, so that a device connected to anetwork 220 can communicate voice, video, audio, images or any otherdata over the network 220. Further, the instructions 212 may betransmitted or received over the network 220 via a communicationinterface 218. The communication interface 218 may be a part of theprocessor 202 or may be a separate component. The communicationinterface 218 may be created in software or may be a physical connectionin hardware. The communication interface 218 is configured to connectwith a network 220, external media, the display 214, or any othercomponents in system 200, or combinations thereof. The connection withthe network 220 may be a physical connection, such as a wired Ethernetconnection or may be established wirelessly as discussed below.Likewise, the additional connections with other components of the system200 may be physical connections or may be established wirelessly.

The network 220 may include wired networks, wireless networks, orcombinations thereof. The wireless network may be a cellular telephonenetwork, an 802.11, 802.16, 802.20, or WiMax network. Further, thenetwork 220 may be a public network, such as the Internet, a privatenetwork, such as an intranet, or combinations thereof, and may utilize avariety of networking protocols now available or later developedincluding, but not limited to, TCP/IP based networking protocols.

Embodiments of the subject matter and the functional operationsdescribed in this specification can be implemented in digital electroniccircuitry, or in computer software, firmware, or hardware, including thestructures disclosed in this specification and their structuralequivalents, or in combinations of one or more of them. Embodiments ofthe subject matter described in this specification can be implemented asone or more computer program products, i.e., one or more modules ofcomputer program instructions encoded on a computer readable medium forexecution by, or to control the operation of, data processing apparatus.While the computer-readable medium is shown to be a single medium, theterm “computer-readable medium” includes a single medium or multiplemedia, such as a centralized or distributed database, and/or associatedcaches and servers that store one or more sets of instructions. The term“computer-readable medium” shall also include any medium that is capableof storing, encoding or carrying a set of instructions for execution bya processor or that cause a computer system to perform any one or moreof the methods or operations disclosed herein. The computer readablemedium can be a machine-readable storage device, a machine-readablestorage substrate, a memory device, or a combination of one or more ofthem. The term “data processing apparatus” encompasses all apparatus,devices, and machines for processing data, including by way of example aprogrammable processor, a computer, or multiple processors or computers.The apparatus can include, in addition to hardware, code that creates anexecution environment for the computer program in question, e.g., codethat constitutes processor firmware, a protocol stack, a databasemanagement system, an operating system, or a combination of one or moreof them.

In a particular non-limiting, exemplary embodiment, thecomputer-readable medium can include a solid-state memory such as amemory card or other package that houses one or more non-volatileread-only memories. Further, the computer-readable medium can be arandom access memory or other volatile re-writable memory. Additionally,the computer-readable medium can include a magneto-optical or opticalmedium, such as a disk or tapes or other storage device to capturecarrier wave signals such as a signal communicated over a transmissionmedium. A digital file attachment to an e-mail or other self-containedinformation archive or set of archives may be considered a distributionmedium that is a tangible storage medium. Accordingly, the disclosure isconsidered to include any one or more of a computer-readable medium or adistribution medium and other equivalents and successor media, in whichdata or instructions may be stored.

In an alternative embodiment, dedicated or otherwise specificallyconfigured hardware implementations, such as application specificintegrated circuits, programmable logic arrays and other hardwaredevices, can be constructed to implement one or more of the methodsdescribed herein. Applications that may include the apparatus andsystems of various embodiments can broadly include a variety ofelectronic and computer systems. One or more embodiments describedherein may implement functions using two or more specific interconnectedhardware modules or devices with related control and data signals thatcan be communicated between and through the modules, or as portions ofan application-specific integrated circuit. Accordingly, the presentsystem encompasses software, firmware, and hardware implementations.

In accordance with various embodiments of the present disclosure, themethods described herein may be implemented by software programsexecutable by a computer system. Further, in an exemplary, non-limitedembodiment, implementations can include distributed processing,component/object distributed processing, and parallel processing.Alternatively, virtual computer system processing can be constructed toimplement one or more of the methods or functionality as describedherein.

Although the present specification describes components and functionsthat may be implemented in particular embodiments with reference toparticular standards and protocols, the invention is not limited to suchstandards and protocols. For example, standards for Internet and otherpacket switched network transmission (e.g., TCP/IP, UDP/IP, HTML, HTTP,HTTPS) represent examples of the state of the art. Such standards areperiodically superseded by faster or more efficient equivalents havingessentially the same functions. Accordingly, replacement standards andprotocols having the same or similar functions as those disclosed hereinare considered equivalents thereof.

A computer program (also known as a program, software, softwareapplication, script, or code) can be written in any form of programminglanguage, including compiled or interpreted languages, and it can bedeployed in any form, including as a standalone program or as a module,component, subroutine, or other unit suitable for use in a computingenvironment. A computer program does not necessarily correspond to afile in a file system. A program can be stored in a portion of a filethat holds other programs or data (e.g., one or more scripts stored in amarkup language document), in a single file dedicated to the program inquestion, or in multiple coordinated files (e.g., files that store oneor more modules, sub programs, or portions of code). A computer programcan be deployed to be executed on one computer or on multiple computersthat are located at one site or distributed across multiple sites andinterconnected by a communication network.

The processes and logic flows described in this specification can beperformed by one or more programmable processors executing one or morecomputer programs to perform functions by operating on input data andgenerating output. The processes and logic flows can also be performedby, and apparatus can also be implemented as, special purpose logiccircuitry, e.g., an FPGA (field programmable gate array) or an ASIC(application specific integrated circuit).

Processors suitable for the execution of a computer program include, byway of example, both general and special purpose microprocessors, andanyone or more processors of any kind of digital computer. Generally, aprocessor will receive instructions and data from a read only memory ora random access memory or both. The essential elements of a computer area processor for performing instructions and one or more memory devicesfor storing instructions and data. Generally, a computer will alsoinclude, or be operatively coupled to receive data from or transfer datato, or both, one or more mass storage devices for storing data, e.g.,magnetic, magneto optical disks, or optical disks. However, a computerneed not have such devices. Moreover, a computer can be embedded inanother device, e.g., a mobile telephone, a personal digital assistant(PDA), a mobile audio player, a Global Positioning System (GPS)receiver, to name just a few. Computer readable media suitable forstoring computer program instructions and data include all forms ofnon-volatile memory, media and memory devices, including by way ofexample semiconductor memory devices, e.g., EPROM, EEPROM, and flashmemory devices; magnetic disks, e.g., internal hard disks or removabledisks; magneto optical disks; and CD ROM and DVD-ROM disks. Theprocessor and the memory can be supplemented by, or incorporated in,special purpose logic circuitry.

To provide for interaction with a user, embodiments of the subjectmatter described in this specification can be implemented on a devicehaving a display, e.g., a CRT (cathode ray tube) or LCD (liquid crystaldisplay) monitor, for displaying information to the user and a keyboardand a pointing device, e.g., a mouse or a trackball, by which the usercan provide input to the computer. Other kinds of devices can be used toprovide for interaction with a user as well. Feedback provided to theuser can be any form of sensory feedback, e.g., visual feedback,auditory feedback, or tactile feedback. Input from the user can bereceived in any form, including acoustic, speech, or tactile input.

Embodiments of the subject matter described in this specification can beimplemented in a computing system that includes a back end component,e.g., a data server, or that includes a middleware component, e.g., anapplication server, or that includes a front end component, e.g., aclient computer having a graphical user interface or a Web browserthrough which a user can interact with an implementation of the subjectmatter described in this specification, or any combination of one ormore such back end, middleware, or front end components. The componentsof the system can be interconnected by any form or medium of digitaldata communication, e.g., a communication network. Examples ofcommunication networks include a local area network (“LAN”) and a widearea network (“WAN”), e.g., the Internet.

The computing system can include clients and servers. A client andserver are generally remote from each other and typically interactthrough a communication network. The relationship of client and serverarises by virtue of computer programs running on the respectivecomputers and having a client-server relationship to each other.

A system may depend on certain rules, logic, and inter-related objectsand data. In technical and computing environments, a system maycalculate values for multiple objects subject to rules, e.g., businessor environment logic, associated with the objects. Certain object typesmay also depend on other object types. For example, a computingenvironment may include multiple objects of different types, e.g., baseobjects and composite objects. A composite object as used herein is anobject whose value depends on, is related to, or is influenced by, thevalues of other objects, such as base objects or other compositeobjects. For example, a composite object may involve transactionsbetween multiple, e.g., two, base objects. Or, a composite object maydefine a relationship between other objects. Thus, composite objectsdepend on the values of other system objects. In one embodiment, acomposite object involves or defines a transaction or relationshipbetween at least two other objects. For example, a composite objectinvolves or defines a transaction or relationship between two baseobjects.

FIG. 3A depicts an illustrative embodiments of an optimization module.FIG. 3A include an identification processor 33, a solutions module 35,an evaluator 37 and an optimization memory 39. The identificationprocessor 33 is connected to the solutions module 35 and theoptimization memory 39. The evaluator 37 is connected the solutionsmodule 35 and the optimization memory 39.

The identification processor 33 may be implemented as a separatecomponent or as one or more logic components, such as on an FPGA whichmay include a memory or reconfigurable component to store logic and aprocessing component to execute the stored logic, or as first logic,e.g. computer program logic, stored in a memory, such as the memory 204shown in FIG. 2 and described in more detail above with respect thereto,or other non-transitory computer readable medium, and executable by aprocessor, such as the processor 202 shown in FIG. 2 and described inmore detail above with respect thereto, to cause the identificationprocessor 33 to, or otherwise be operative to identify from the set ofbase data objects, a core data object therein, the core data objectincluding object value data corroborated by one or more externalsources. In one embodiment, the one or more external sources may includethe trade database 108, market data module 112 (not shown), or accountdata module 104 (not shown) among others.

The solutions module 35 may be implemented as a separate component or asone or more logic components, such as on an FPGA which may include amemory or reconfigurable component to store logic and a processingcomponent to execute the stored logic, or as second logic, e.g. computerprogram logic, stored in a memory, such as the memory 204 shown in FIG.2 and described in more detail above with respect thereto, or othernon-transitory computer readable medium, and executable by a processor,such as the processor 202 shown in FIG. 2 and described in more detailabove with respect thereto, to cause the solutions module 35 to, orotherwise be operative to generate from the set of composite dataobjects a first subset of composite data objects comprising at least afirst composite data object including the particular base data objectand a second composite data object including the identified core dataobject. The solutions module 35 is further configured to generate fromthe set of composite data objects a second subset of composite dataobjects comprising at least a third composite data object including theparticular base data object and a fourth composite data object includingthe identified core data object.

The evaluator 37 may be implemented as a separate component or as one ormore logic components, such as on an FPGA which may include a memory orreconfigurable component to store logic and a processing component toexecute the stored logic, or as third logic, e.g. computer programlogic, stored in a memory, such as the memory 204 shown in FIG. 2 anddescribed in more detail above with respect thereto, or othernon-transitory computer readable medium, and executable by a processor,such as the processor 202 shown in FIG. 2 and described in more detailabove with respect thereto, to cause the evaluator 37 to, or otherwisebe operative to receive data indicative of a level of activity for eachof a plurality of composite data objects in the first and second subsetsof composite data objects. The evaluator 37 may receive data fromexternal sources such as the trade database 108, market data module 112(not shown), account data module 104 (not shown) or other data sourceand may the same or different from the external sources from which theobject value data of the identified core data object was corroborated.The evaluator 37 is further configured to derive a ranking value of thefirst and second subsets. The evaluator 37 is configured to select oneof the subsets as an optimal solution.

The optimization memory 39 may be implemented as a separate component oras one or more logic components, such as on an FPGA which may include amemory or reconfigurable component to store logic and a processingcomponent to execute the stored logic, or as fourth logic, e.g. computerprogram logic, stored in a memory, such as the memory 204 shown in FIG.2 and described in more detail above with respect thereto, or othernon-transitory computer readable medium, and executable by a processor,such as the processor 202 shown in FIG. 2 and described in more detailabove with respect thereto, to cause the optimization memory 39 to, orotherwise be operative to store the base objects and the compositeobjects. The base objects and composite objects may be stored as dataobjects in a memory or a database. The data objects may be stored in thecomputer's memory in any convenient form or structure, such as a linkedlist, or through the use of pointers. The base objects and compositeobjects may be stored using nodes, links, and attributes. The baseobjects and composite objects may be store in a table. FIG. 3Billustrates an example representation of the relationships of three baseobjects stored in a database in the optimization memory 39. The threebase objects and the relationships between these base objects may bestored in a table or linked list. The relationships between the threebase objects may be stored as attributes for the base objects or asseparate data objects in the optimization memory 39. In the depictedexample, there are three base objects, as shown in FIG. 3B: A, B, and C,however it will be appreciated that there may be fewer or more baseobjects as described herein. Each of the base objects may be given,assigned, characterized by or otherwise ascribed an estimated value.E.g. the value for A may be 5, the value for B may be 100, and the valuefor C may be 50. The values for each of the base objects may representan estimate of the value of the base object. The estimate may becalculated by the settlement management module 140 as a function ofreceived value data from, for example, the trade engine 138 or tradedatabase 108. It should be appreciated that that the received values forbase objects may be considered to be subjective values for those baseobjects, or based on a user's perception of the base objects. Theoptimization module 142, in one embodiment, selects one of the receivedsubjective values for the base objects to determine a best objectivevalue for each base object, where the value selected as the final valueis one of the received values. In other words, the optimization module142 selects one of multiple subjective values as representative of anobjective value for that object.

The estimated value for each of A, B, and C may be estimated as varyinglevels of confidence based on the received data. Certain objects may betransacted multiple times over a period of time, with the value of theobjects changing accordingly. With multiple samples of data frommultiple transactions, the value of the object may be estimated with ahigh level of confidence. However, certain objects may only betransacted a few times or not at all in a given period of time. Theestimated value for the objects may be uncertain or indeterminate. Onesolution generated by the solutions module 35 may be to derive the valueof the object from the values of objects that are related to the object.

FIG. 3B, for example, depicts three composite objects AB, BC, and AC.Each of the composite objects may be one permutation out of the possiblecombinations of the base objects A, B, and C. For example, compositeobject AB may represent a transaction including both A and B. CompositeBC may represent a transaction including both B and C. Differentpermutations may be possible, such as a composite objects with more thantwo base objects, a composite object that includes multiple quantitiesof a base object (e.g. A2B that includes one A and two B), andrelationships using other mathematical concepts. As with the baseobjects, each of the composite objects AB, BC, and AC may be assigned avalue derived from data received by the evaluator 37. The data receivedmay value the composite objects as a whole and not the individualcomponents. For example, the composite object AB may be transacted andassigned a price as the combination of AB instead of valuing eachindividual piece or leg of the composite object and then calculating thevalue. However, the value of the composite objects may be closelyrelated to the value of the underlying base objects. As such, anestimate of the value of the base objects may be derived from thecomposite objects and vice versa.

In FIG. 3B, the three base objects are related to one another throughthe composite objects. Accordingly, an estimate of the value of each ofthe base objects may be derived from the other base objects or the valueof one or more of the composite objects. For example, if the value ofthe base object B is not known, but the value of A and the value of thecomposite object AB are known, then the value of B may be calculated bysubtracting the value of A from the value of AB (if composite object ABrepresents A+B). Alternative solutions of calculating the value of B mayexist depending on the known values. The value of base object B may alsobe calculated if the value of A and the values of composite objects ACand BC are known. The value of base object B may be calculated by BCminus AC plus A. The value of B may be calculated if C is known by usingthe value of BC. The value of B may be calculated if C is known by usingthe values of AC and AB.

Each of these solutions may derive a different value for B. With up todate and fully vetted data and values, each of the solutions couldarrive at the same value. However, for many of the objects, the knowndata may be insufficient. Certain objects or composite objects may besparely transacted by only a few parties. Additionally, the amount andquality of data for each of the objects and composite objects vary overtime. Using the examples in FIG. 3B, the object B may be calculated,using multiple solutions generated by the solutions module 35. For thisexample, the object A may be considered a core object. The processor 35may identify a core object based on a level of activity or interestcorroborated by an external source. For example, a core object may be anobject that has a value that is widely agreed upon by external parties,e.g. a benchmark object. The value for a core object may still be anestimate, but the value may be estimated with a high level ofconfidence. Core objects may also be given or assigned a value (or useinputs) from a reputable source.

With the value of core object A known, the solutions module 35 maygenerate two solutions that may be used to derive the value of object B.The first solution uses a value of AB. The second solution uses thevalues of AC and BC. The first solution and the second solution mayderive different values object B. For example, the composite object ABmay be sparely transacted. If the last transaction of composite AB wasdone at a first time and the value of object B is to be estimated at alater time, the estimate may be off. For example, the last transactionof AB was recorded several hours prior to when the value of object B iscalculated. The composite objects AC and BC may be transacted more oftenor the composite objects AC and BC may be transacted by more parties orwith larger volumes or revenues. In either scenario, the solution thatincorporates the values of AC and BC may estimate the value of B withhigher confidence. The optimization memory 39 may store the solutions asdata objects. The optimization memory 39 may remove or purge or set tobe overwritten solutions that are non-optimal.

In one embodiment, a computing system may receive values for compositeobjects, and the computing system attempts to determine values for thebase objects in accordance with relationships associated with the baseobjects and composite objects. In particular, the system may receiveuser interest for the composite objects. The user interest may be usedas a confidence indicator for pricing of the composite object. A highuser interest may indicate a high confidence that a composite object'sprice is indicative of the value of the composite object and underlyingbase objects.

In an embodiment, a computing system identifies a first subset ofcomposite data objects. The computer system identifies a second subsetof composite data objects. The computing system evaluates each of thefirst and second subset and selects the optimal subset. The first andsecond subsets may correspond to two different solutions for calculatinga value of an object. The evaluation may use received market data suchas revenue or broker marks.

In an embodiment, a computing system may receive values for compositeobjects that indicate the revenue for each composite object. Thecomposite objects are evaluated based on the received revenue data. Theevaluation of the composite objects may be used to identify a solutionfor calculating the value of a base object.

In an embodiment, a computing system identifies a set of transactedcomposite objects and assigns a ranking value to each composite objectin the set based on received data. Low rated composite objects areexcluded. The remaining set of composite objects is used to generate agraph for one or more base objects. The computing system generates amaximum spanning tree from the graph that identifies one or more pathsthat may be used to estimate values of base objects.

In an embodiment, the optimization module is configure to rapidly andefficiently determine solutions for estimating values for objects byusing the values of composite objects that meet predetermined rules forthe composite objects. Predetermined rules may be programmed into thecomputer. An example predetermined rule may be a rule about the volumeof transactions or number of users or user interest. The rules may behard rules, or system requirements. Alternatively, rules may be softrules that are not requirements but reflect system or user preferences.

When applied to a financial exchange computer system, the embodimentsdescribed herein may utilize trade related electronic messages such asmass quote messages, individual order messages, modification messages,cancelation messages, etc., so as to enact trading activity in anelectronic market. The trading entity and/or market participant may haveone or multiple trading terminals associated with the session.Furthermore, the financial instruments may be financial derivativeproducts. Derivative products may include futures contracts, options onfutures contracts, futures contracts that are functions of or related toother futures contracts, swaps, swaptions, or other financialinstruments that have their price related to or derived from anunderlying product, security, commodity, equity, index, or interest rateproduct. In one embodiment, the orders are for options contracts thatbelong to a common option class. Orders may also be for baskets,quadrants, other combinations of financial instruments, etc. The optioncontracts may have a plurality of strike prices and/or comprise put andcall contracts. A mass quote message may be received at an exchange. Asused herein, an exchange 100 includes a place or system that receivesand/or executes orders.

It should be appreciated that the disclosed embodiments may use othertypes of messages depending upon the implementation. Further, themessages may comprise one or more data packets, datagrams or othercollection of data formatted, arranged configured and/or packaged in aparticular one or more protocols, e.g., the FIX protocol, TCP/IP,Ethernet, etc., suitable for transmission via a network 214 as wasdescribed, such as the message format and/or protocols described in U.S.Pat. No. 7,831,491 and U.S. Patent Publication No. 2005/0096999 A1, bothof which are incorporated by reference herein in their entireties andrelied upon. Further, the disclosed message management system may beimplemented using an open message standard implementation, such as FIXBinary, FIX/FAST, or by an exchange-provided API.

Traders trading on an exchange including, for example, exchange computersystem 100, often desire to trade multiple financial instruments incombination. Each component of the combination may be called a leg.Traders can submit orders for individual legs or in some cases cansubmit a single order for multiple financial instruments in anexchange-defined combination. Such orders may be called a strategyorder, a spread order, or a variety of other names. The legs may bereferred to as base objects; the combinations may be referred to ascomposite objects. An exchange computer system may not offer each andevery possible combination, rather only a subset of the possiblecombinations referred to as exchange-defined combinations.

A spread instrument may involve the simultaneous purchase of onesecurity and sale of a related security, called legs, as a unit. Thelegs of a spread instrument may be options or futures contracts, orcombinations of the two. Trades in spread instruments are executed toyield an overall net position whose value, called the spread, depends onthe difference between the prices of the legs. Spread instruments may betraded in an attempt to profit from the widening or narrowing of thespread, rather than from movement in the prices of the legs directly.Spread instruments are either “bought” or “sold” depending on whetherthe trade will profit from the widening or narrowing of the spread,respectively. An exchange often supports trading of common spreads as aunit rather than as individual legs, thus ensuring simultaneousexecution of the two legs, eliminating the execution risk of one legexecuting but the other failing.

One example of a spread instrument is a calendar spread instrument. Thelegs of a calendar spread instrument differ in delivery date of theunderlier. The leg with the earlier occurring delivery date is oftenreferred to as the lead month contract. A leg with a later occurringdelivery date is often referred to as a deferred month contract. Anotherexample of a spread instrument is a butterfly spread instrument, whichincludes three legs having different delivery dates. The delivery datesof the legs may be equidistant to each other. The counterparty ordersthat are matched against such a combination order may be individual,“outright” orders or may be part of other combination orders.

In other words, an exchange may receive, and hold or let rest on thebooks, outright orders for individual contracts as well as outrightorders for spreads associated with the individual contracts. An outrightorder (for either a contract or for a spread) may include an outrightbid or an outright offer, although some outright orders may bundle manybids or offers into one message (often called a mass quote).

A spread is an order for the price difference between two contracts.This results in the trader holding a long and a short position in two ormore related futures or options on futures contracts, with the objectiveof profiting from a change in the price relationship. A typical spreadproduct includes multiple legs, each of which may include one or moreunderlying financial instruments. A butterfly spread product, forexample, may include three legs. The first leg may consist of buying afirst contract. The second leg may consist of selling two of a secondcontract. The third leg may consist of buying a third contract. Theprice of a butterfly spread product may be calculated as:

Butterfly=Leg1−2×Leg2+Leg3  (equation 1)

In the above equation, Leg1 equals the price of the first contract, Leg2equals the price of the second contract and Leg3 equals the price of thethird contract. Thus, a butterfly spread could be assembled from twointer-delivery spreads in opposite directions with the center deliverymonth common to both spreads.

A calendar spread, also called an intra-commodity spread, for futures isan order for the simultaneous purchase and sale of the same futurescontract in different contract months (i.e., buying a September CME S&P500® futures contract and selling a December CME S&P 500 futurescontract).

A crush spread is an order, usually in the soybean futures market, forthe simultaneous purchase of soybean futures and the sale of soybeanmeal and soybean oil futures to establish a processing margin. A crackspread is an order for a specific spread trade involving simultaneouslybuying and selling contracts in crude oil and one or more derivativeproducts, typically gasoline and heating oil. Oil refineries may trade acrack spread to hedge the price risk of their operations, whilespeculators attempt to profit from a change in the oil/gasoline pricedifferential.

A straddle is an order for the purchase or sale of an equal number ofputs and calls, with the same strike price and expiration dates. A longstraddle is a straddle in which a long position is taken in both a putand a call option. A short straddle is a straddle in which a shortposition is taken in both a put and a call option. A strangle is anorder for the purchase of a put and a call, in which the options havethe same expiration and the put strike is lower than the call strike,called a long strangle. A strangle may also be the sale of a put and acall, in which the options have the same expiration and the put strikeis lower than the call strike, called a short strangle. A pack is anorder for the simultaneous purchase or sale of an equally weighted,consecutive series of four futures contracts, quoted on an average netchange basis from the previous day's settlement price. Packs provide areadily available, widely accepted method for executing multiple futurescontracts with a single transaction. A bundle is an order for thesimultaneous sale or purchase of one each of a series of consecutivefutures contracts. Bundles provide a readily available, widely acceptedmethod for executing multiple futures contracts with a singletransaction.

Thus an exchange may match outright orders, such as individual contractsor spread orders (which as discussed above could include multipleindividual contracts). The exchange may also imply orders from outrightorders. For example, exchange computer system 100 may derive, identifyand/or advertise, publish, display or otherwise make available fortrading orders based on outright orders.

For example, two different outright orders may be resting on the books,or be available to trade or match. The orders may be resting becausethere are no outright orders that match the resting orders. Thus, eachof the orders may wait or rest on the books until an appropriateoutright counteroffer comes into the exchange or is placed by a user ofthe exchange. The orders may be for two different contracts that onlydiffer in delivery dates. It should be appreciated that such orderscould be represented as a calendar spread order. Instead of waiting fortwo appropriate outright orders to be placed that would match the twoexisting or resting orders, the exchange computer system may identify ahypothetical spread order that, if entered into the system as a tradablespread order, would allow the exchange computer system to match the twooutright orders. The exchange may thus advertise or make available aspread order to users of the exchange system that, if matched with atradable spread order, would allow the exchange to also match the tworesting orders. Thus, the match engine is configured to detect that thetwo resting orders may be combined into an order in the spreadinstrument and accordingly creates an implied order.

In other words, the exchange's matching system may imply thecounteroffer order by using multiple orders to create the counterofferorder. Examples of spreads include implied IN, implied OUT, 2nd- ormultiple-generation, crack spreads, straddle, strangle, butterfly, andpack spreads. Implied IN spread orders are derived from existingoutright orders in individual legs. Implied OUT outright orders arederived from a combination of an existing spread order and an existingoutright order in one of the individual underlying legs. Implied orderscan fill in gaps in the market and allow spreads and outright futurestraders to trade in a product where there would otherwise have beenlittle or no available bids and asks.

For example, implied IN spreads may be created from existing outrightorders in individual contracts where an outright order in a spread canbe matched with other outright orders in the spread or with acombination of orders in the legs of the spread. An implied OUT spreadmay be created from the combination of an existing outright order in aspread and an existing outright order in one of the individualunderlying leg.

By linking the spread and outright markets, implied spread tradingincreases market liquidity. For example, a buy in one contract month andan offer in another contract month in the same futures contract cancreate an implied market in the corresponding calendar spread. Anexchange may match an order for a spread product with another order forthe spread product. Some existing exchanges attempt to match orders forspread products with multiple orders for legs of the spread products.With such systems, every spread product contract is broken down into acollection of legs and an attempt is made to match orders for the legs.Examples of implied spread trading includes those disclosed in U.S.Patent Publication No. 2005/0203826, entitled “Implied Spread TradingSystem,” the entire disclosure of which is incorporated by referenceherein and relied upon. Examples of implied markets include thosedisclosed in U.S. Pat. No. 7,039,610, entitled “Implied Market TradingSystem,” the entire disclosure of which is incorporated by referenceherein and relied upon.

As an intermediary to electronic trading transactions, the exchangebears a certain amount of risk in each transaction that takes place. Tothat end, the clearing house implements risk management mechanisms toprotect the exchange. One or more of the modules of the exchangecomputer system 100 may be configured to determine settlement prices forconstituent contracts, such as deferred month contracts, of spreadinstruments, such as for example, settlement module 140.

One or more of the above-described modules of the exchange computersystem 100 may be used to gather or obtain data to support thesettlement price determination, as well as a subsequent marginrequirement determination. For example, the order book module 110 and/orthe market data module 112 may be used to receive, access, or otherwiseobtain market data, such as bid-offer values of orders currently on theorder books. The trade database 108 may be used to receive, access, orotherwise obtain trade data indicative of the prices and volumes oftrades that were recently executed in a number of markets. In somecases, market data (and/or bid/ask data) may be gathered or obtainedfrom open outcry pits and/or other sources and incorporated into thetrade and market data from the electronic trading system(s).

In order to minimize risk to the exchange while minimizing the burden onmembers, it is desirable to approximate the requisite performance bondor margin requirement as closely as possible to the actual positions ofthe account at any given time. An exchange may use a settlement methodto determine the position of a contract.

In some cases, the outright market for the deferred month or otherconstituent contract may not be sufficiently active to provide marketdata (e.g., bid-offer data) and/or trade data. Spread instrumentsinvolving such contracts may nonetheless be made available by theexchange. The market data from the spread instruments may then be usedto determine a settlement price for the constituent contract. Asettlement module 140 of the exchange computer system 100 may implementone or more settlement price solutions. The settlement pricedetermination techniques or solution may be generated by theoptimization module. The optimization module may attempt to arrive at asolution by using related objects, spreads or products. The optimizationmodule attempts to objectively value the inactive object by usingrelated objects that are active enough so that any information or valuedata derived from the active related objects may be used with a level ofconfidence in evaluated the original base object.

One inefficient method for selecting a settlement price determination isto take a simple path from a core object. Such a path may be calculatedautomatically, using for example, a shortest path algorithm. A simplepath may also be manually generated and selected by a user.

FIG. 4 depicts an example graph of base objects and composite objects.The graph illustrates the relationships between the base objects 47 asdefined by the composite objects 49. The vertices 47 of the graphrepresent the base objects 47 (outright products) that are offered on anexchange system. The edges 49 of the graph represent the compositeobjects 49 (spreads etc.) that are offered on the exchange system. Thecomposite objects 49 that are offered on the exchange system (exchangeddefined combinations) are a subset of the total combinations of the baseobjects possible.

FIG. 4 further includes a core product 51, here represented by the baseobject FD. A core product may be a product that is a benchmark futureslike NYMEX WTI futures, NYMEX RBOB futures, ICE Brent futures amongothers. A core product may also represent a product that is very welltraded. Accordingly, the price of the core product is well accepted bythe community as a benchmark.

FIG. 4 illustrates ten base objects 47 labeled as XM, XK, XA, XN, NX,VL, GH, XF, VJ, and FD. Each of these base objects may be transacted bythemselves (outright) or in combination with one another (as spreadsetc.). For the ten base objects, there exist thousands upon thousands ofpossible combinations that may be transacted. An exchange system mayonly transact in a small subset of the possible combinations asexchanged defined combinations. In FIG. 4, there are 13 exchange definedcombinations (referred to as composite objects 49) shown. In the graph45, the composite objects 49 are shown as edges between the base objects47 and core object 51. The value of the composite objects 49 may bepriced by broker marks e.g. by using the average of broker data oranother solution.

For the example shown in FIG. 4, each of the composite objects 49 mayrepresent a spread, e.g. a combination of two or more base objects. Thethirteen spreads are described below in Table 1. Each of the combinationobjects has a first leg and a second, leg e.g. the base objects thatmake up the spread.

TABLE 1 Spread code Leg 1 Leg 2 HB XF XA FH XM XK IP XN XA IT XA FD IXXK XA VI XF VJ IN XK FD GG FD GH HH VJ XA UP NX VL IQ XN FD NI NX GH VHXF FD VN VJ GH VX XF GH

FIG. 5 depicts a simple pricing complex 55 from the core object 51. Asshown, each of the ten base objects may be priced by taking a simplepath using the composite objects to the core object 51. For example, theprice XA, the solution uses the composite object XA-FD. To price VJ, thesolution uses the composite object VJ-XF and XF-FD. Certain compositeobjects in the graph 45 of FIG. 4 are not used by any of the solutions.For example, the composite object XA-XN is not used. There may bealternative simple solutions that include similar path lengths. Forexample, an alternative set of spreads uses may include XA-VJ instead ofXF-VJ.

There may me multiple methods for evaluating the solutions as shown inFIG. 5. One method may be to calculate the total interest or the totalrevenue for either the individual solutions or the product complex as awhole.

For this example, the following values may be used:

TABLE 2 Total open interest for the products in the pricing routes FH XMXK 320 IN XK FD 480 IT XA FD 8230 IQ XN FD 2 GG FD GH 268 PP NX GH 0 VHXF FD 0 VI XF VJ 4988 UP NX VL 3546 total 17834

TABLE 3 Total revenue from the products in the pricing routes FH XM XK$16,205.00 IN XK FD $24,902.00 IT XA FD $194,613.00 IQ XN FD $795.00 PPNX GH 0 GG FD GH $17,469.00 VH XF FD $127.00 VI XF VJ $106,665.00 UP NXVL $86,514.00 total $447,290.00

For the simple pricing complex 55 described in FIG. 5, the total openinterest is 17,834. The total revenue is $447,290. These values maychange over time as the underlying open interest or revenue changes forthe composite objects. Additionally if other composite objects aresubstituted in, the total revenue and open interest of the pricingcomplex may change. Alternative methods for evaluation may be used. Forexample, the open interest may be calculated for each base product andthen totaled. Such a calculation may purposefully double count certaincomposite objects that are used more often than others. Methods mayweight one or more composite objects differently that have a higherlevel of confidence. In certain embodiment, market data received latermay be treated differently than market data received earlier (or furtheraway from when a settlement price is to be calculated). For example,data received early in the day may be less indicative of an accuratevalue that is estimated at the close of business.

One priority for generating the path 55 such as the one in FIG. 5 is tomaximize the interest or revenue for the pricing complex. Higherinterest and/or higher revenue may indicate a pricing complex that ismore efficient and accurate at pricing individual objects than a pricingcomplex that has lower revenue or lower interest. The optimizationmodule 142 is trying to objectively value an object which has asubjective value. For objects that have insufficient interest orrevenue, a value may still be derived from related objects that haveexternal/subjective/independent corroboration of value.

In an embodiment, the optimization module 142 identifies each possiblesolution for a pricing complex. The optimization module 142 evaluates orscores the solutions for each composite object. The optimization module142 selects an optimal pricing complex for the product or the productcomplex. The optimization module 142 transmits the selected pricingcomplex to the settlement module 140.

FIG. 6 depicts an example flowchart 600 indicating a method ofimplementing the disclosed optimization system, as may be implementedwith computer devices and computer networks, such as those describedwith respect to FIGS. 1 and 2. Embodiments may involve all, more orfewer actions indicated by the blocks of FIG. 6. The actions may beperformed in the order or sequence shown or in a different sequence.

At act A110, the optimization module 142 identifies a set of baseobjects and a set of composite objects offered by the exchange. In anembodiment, a base object may represent an outright product and acomposite object may represent a product spread. The composite objectsmay include the base objects. For example, a composite object AB may becomprised of base object A and base object B.

FIG. 4 illustrates a set of base objects and a set of composite objectsoffered by the exchange. As shown, FIG. 4 includes base objects 47 XM,XK, XA, XN, NX, VL, GH, XF, VJ, and FD. FIG. 4 further includesseventeen composite objects 49 represented by the edges or connectionsbetween base objects 47. A composite object 49 may be defined by thecomponents included within e.g. the base objects 47. However, acomposite object 49 may be traded on its own and with its own market andorder book. As such, while a composite object's price may be related tothe component base objects, there may be some variation in pricingbetween the composite object and the total of the component baseobjects.

At act A120, the optimization module receives market data for thecomposite objects 49. The market data may include a level of activitysuch as revenue for each of the composite objects 49 for a time period.The market data may include the number of brokers transacting thecomposite object. The market data may include trading volume, openinterest, transaction data or other market data. The market data may bestored in memory, in the optimization module, or another module.

At act A130, the optimization module derives a score or ranking valuefor each composite object 49 using the received market data. The rankingvalue may be calculated as a direct ranking of one or more values fromthe market data. For example, the composite objects 49 may be ranked byrevenue with the composite object with the highest revenue given thehighest ranking. In another embodiment, the ranking may be derived frommultiple values from the market data. For example, the ranking value maybe calculated using the following equation: Ranking=#of brokerssubmitting marks for that composite object+α. More interest or activityin the composite object indicates higher confidence in the estimatedvalue of the composite object and as such the composite object isassigned a higher ranking.

In an embodiment, the values of α for each composite object 49 aredetermined using either revenue from the composite object or openinterest as described in Table 4 below.

TABLE 4 α value table α Revenue from the composite object revenue <$50000 0 or # of broker marks = 0 $50000 ≤ revenue < $100000 1 $100000 ≤revenue 2 Composite object open interest open interest < 2000 0 or # ofbroker marks = 0 3000 ≤ open interest < 1 5000 5000 ≤ open interest 2

Table 5 below shows an example set of rank values for each compositeobject 49.

TABLE 5 #of Composite open broker Object Leg 1 Leg 2 Revenue interestmarks Ranking HB XF XA 114085 5654 4 8 FH XM XK 16205 320 5 5 IP XN XA145007 6354 3 7 IT XA FD 194613 8230 3 7 IX XK XA 112315 5020 3 7 VI XFVJ 106665 4988 3 6 IN XK FD 24902 480 3 3 GG FD GH 17469 268 1 1 HH VJXA 77861 3877 0 0 UP NX VE 86514 3546 1 1 IQ XN FD 795 2 0 0 NI NX GH756 15 0 0 VH XF FD 127 0 0 0 VN VJ GH 5 0 0 0 VX XF GH 573 13 0 0

In certain embodiment, the ranking may be a direct reflection of themarket data received. For example, the ranking may be a numericalranking of the revenue of the composite object 49 or the open interest.In certain embodiments, a scale of rank values is used where a highranking is optimal. In certain embodiment, a scale is used where a lowranking is optimal. In certain embodiments, the rank values increaselinearly. In certain embodiment, the rank values increase exponentially.For example, a ranking value of 6 may indicate a medium level ofconfidence. A ranking value of 12 may or may not indicate double thelevel of confidence depending on if the scale of the ranking system islinear or non-linear. Using ranking values, the composite objects 49 maybe separated into buckets or categories such as low, medium, and high.

FIG. 7 depicts an example rated graph 75. FIG. 7 includes exampleranking values for each composite object according to Table 5. Eachcomposite object 49 has a rank 71. For example, the composite objectwith XK and FD (listed above in the table as composite object IN) as itsbase objects has a rank of 3.

At act A140, the optimization module identifies possible solutions forcalculating a value for one or more of the base objects 47. Theoptimization module 142 may identify one or more core objects 51 amongthe base objects 47. A core object is an object that has a known valueor an estimated value that is generally agreed upon by a market. Coreobjects may be, for example, benchmark futures. Each core product may betransacted often enough for a system to estimate its value with a highlevel of confidence at for any specific time period. The value of a coreobject may also be corroborated directly by external sources orindirectly though external value data. Accordingly, the value of a coreproduct may be considered “known” to an exchange system and anoptimization module 142.

From the core product 51, the optimization module 142 may generatepossible solutions for calculating the value of the base objects. Asshown in FIG. 4, each composite object connects (or describes arelationship) between two base objects. In certain embodiments, thecomposite objects may include three or more base objects or othercombinations. Using the composite objects, or relationships, theoptimization may determine one or more paths from an object to a coreobject. For example, in FIG. 4, multiple paths may be taken from VJ toFD; e.g. VJ to GH to FD; VJ to XF to FD; VJ to XA to XN to FD; amongothers.

In certain embodiments, composite objects that have low rank values maybe excluded from the possible solutions. A threshold rank may be uses asa cutoff to exclude low scoring composite objects. For example, for thecomposite objects 49 of FIG. 7, the composite objects with a zero rankmay be excluded. A rank of zero may indicate low number of broker marks,low open interest, low volume, or low revenue depending on the data usedto generate the rank. A low rank may also indicate that the estimatedvalue of the composite object is not estimated with a high level ofconfidence. Accordingly, when using an estimated value of the compositeobject to estimate a value of a base object, there is a preference forhigh scoring composite objects in order to accurately price the baseobject. A high rank may indicate that the data supplied by the compositeobject for an eventual solution is of high quality. The high rank mayindicate that external source have corroborated the estimated value ofthe composite object either by showing interest directly or indirectly.

A solution including multiple composite objects may only allow for aconfidence level of the lowest scoring composite object. For example, asolution includes three composite objects. A, B, and C and both A and Brate high but C scores low. The overall confidence level in the solutionis brought down to the level of C. If C is off by 50%, the final valuecalculated by the solution may also be off by at least 50% (depending onthe accuracy of A and B).

At act A150, the optimization module evaluates the solutions. Thesolutions may include one or more composite objects. The rank values foreach of the composite objects calculated in A150 may be used tocalculate a rank value for the solution. In certain embodiments, therank value for a solution is the total score of each of the compositeobjects that make up the solution. For example, in FIG. 7 there aremultiple solutions for calculating the value of VJ from the core objectFD. Using the total of all composite objects included in the path, therank value for the path VJ-XF-FD may be 6. The rank value for VJ-GH-FDis 1. The rank value for VJ-XF-XA-FD is 21.

In certain embodiments, the rank value for a solution is calculated bytaking the rank value of each of the composite objects, adding the rankvalues together and dividing the total by the number of compositeobjects to determine an average rank value. For example, in FIG. 7 thereare multiple solutions for calculating the value of VJ from the coreobject FD. Using the average of all composite objects included in thepath, the rank value for the path VJ-XF-FD may be 3. The rank value forVJ-GH-FD is 0.5. The rank value for VJ-XF-XA-FD is 7.

In certain embodiments, the rank value for a solution reflects thelowest rated composite object in the solution, e.g. the composite objectthat is least corroborated by external sources. For example, in FIG. 7there are multiple solutions for calculating the value of VJ from thecore object FD. Using the lowest ranking composite object of allcomposite objects included in the path, the ranking for the pathVJ-XF-FD may be 0. The ranking for VJ-GH-FD is 0. The ranking forVJ-XF-XA-FD is 6.

In certain embodiments, the ranking is based on the number of compositeobjects in the solution. For example, in FIG. 7 there are multiplesolutions for calculating the value of VJ from the core object FD. Usingthe total number of all composite objects included in the path, theranking for the path VJ-XF-FD may be 2. The ranking for VJ-GH-FD is 2.The ranking for VJ-XF-XA-FD is 3.

At act A160, the optimization module selects one or more optimalsolutions. The optimization module may use the rank values generated inact A150 to determine which solution is optimal for each individual baseobject. Alternatively, the optimization module may determine a rankingvalue for an entire product complex and select solutions based on theranking. The one or more optimal solutions may be stored in memory.Alternative non-optimal solutions may be removed from or designated tobe overwritten in memory.

The value of a base object may be calculated using the optimal solution.The value may be calculated by the settlement module or by theoptimization module and then reported to the settlement module. Aconfidence level may be identified that relates to the ranking of thesolution. The confidence level may be transmitted to the settlementmodule along with the value of the object or the solution.

One or more of these acts may be repeated over time. For example, avalue for each object may be recalculated at the end of the day or atvarious points during the day. The optimization module may receive datathroughout the day and may recalculate ranking values for each of thecomposite objects.

For certain large sets of objects, the above described flowchart may bealtered by limiting the number of solutions that are rated or scored.FIG. 8 depicts an example flowchart 800 indicating a method ofimplementing the disclosed optimization system using a maximum spanningtree, as may be implemented with computer devices and computer networks,such as those described with respect to FIGS. 1 and 2. Embodiments mayinvolve all, more or fewer actions indicated by the blocks of FIG. 8.The actions may be performed in the order or sequence shown or in adifferent sequence.

At act A210, an exchange system generates a product complex from a setof base objects and a set of composite objects offered by the exchangesystem. The composite objects may comprise a subset of possiblecombinations of the base objects. Each of the composite objects, forexample, may include two or more base objects. For example, a compositeobject AB may include both base objects A and B. Each composite objectmay be transacted separately from the outright base objects and as suchmay have a value that while related to the base objects, may differ atcertain points in time. As such, the value of the composite object maybe estimated from the values of the base objects and vice versa.

The set of base objects may include one or more core products. Theproduct complex may be limited to a subset of base objects and compositeobjects that are related to one or more of the core products. In certainembodiments, the base objects and composite objects connected to a coreproduct may comprise a sub-group of the product complex. For example, inFIG. 4, there is a single core product.

FIG. 9 depicts a product complex 91 including multiple core products 51.In FIG. 9 there are two core products 91 labeled as FD and TL. Each ofthe base objects 47 that are connected to the first core product FD maycomprise a first sub-group 93. Each of the base objects that areconnected to the second core object TL may comprise a second sub-group95. In certain embodiments, there may be overlap between productcomplexes. For example, a base object and/or a composite object may beincluded in both sub-groups. As shown by FIG. 9 the base object XA isincluded in both the first sub-group 93 and the second sub-group 95. Incertain embodiments, the sub-groups may be defined manually orautomatically by following pre-set rules or preferences. For example,the sub-groups may be limited to base objects within a threshold numberof connections from a core object.

The relationship of the base objects and composite objects may beillustrated by using a graph. FIG. 9 illustrates an example graph ofbase objects 47 and composite objects 49. The base objects arerepresented by the nodes or vertices. The composite objects arerepresented by the links or edges.

At act A220, the exchange system receives market data for one or morecomposite objects 49. Market data may include data indicative of a levelof activity or a number of participants; data such as revenue, volume,interest, number of brokers, or other market data. Table 6 belowillustrates example revenue, open interest and #of broker marks for thecomposite objects 49 of the first sub group 93 in FIG. 9.

TABLE 6 open #of broker Composite Object Leg 1 Leg 2 Revenue interestmarks HB XF XA 114085 5654 4 FH XM XK 16205 320 5 IP XN XA 145007 6354 3IT XA FD 194613 8230 3 IX XK XA 112315 5020 3 VI XF VJ 106665 4988 3 INXK FD 24902 480 3 GG FD GH 17469 268 1 HH VJ XA 77861 3877 0 UP NX VL86514 3546 1 IQ XN FD 795 2 0 NI NX GH 756 15 0 VH XF FD 127 0 0 VN VJGH 5 0 0 VX XF GH 573 13 0

At act A230, the exchange system evaluates the one or more compositeobjects using the market data. The market data may be used to ascertainif the data from a composite object is accurate. For example, the valueof a composite object may be verified in that external sources haveactively traded the composite object at a particular value. An activelytraded or transacted or watched composite objects may be less prone touser's exploiting price differences. In the example illustrated in FIG.9, the rank value for the composite object is calculated by adding α, β,and the #of broker marks. The values of α and 13 are generated by thefollowing table:

TABLE 7 α value table Revenue from the product α revenue < $50000 or #of broker marks = 0 0 $50000 ≤ revenue < $100000 1 $100000 ≤ revenue 2 βvalue table product open interest β open interest < 2000 or # of brokermarks = 0 0 3000 ≤ open interest < 5000 1 5000 ≤ open interest 2

The rank values for the composite objects are as follows:

TABLE 8 #of Composite open broker Rank Object Revenue α interest β marksvalue HB 114085 2 5654 2 4 8 FH 16205 0 320 0 5 5 IP 145007 2 6354 2 3 7IT 194613 2 8230 2 3 7 IX 112315 2 5020 2 3 7 VI 106665 2 4988 1 3 6 IN24902 0 480 0 3 3 GG 17469 0 268 0 1 1 HH 77861 0 3877 0 0 0 UP 86514 03546 0 1 1 IQ 795 0 2 0 0 0 NI 756 0 15 0 0 0 VH 127 0 0 0 0 0 VN 5 0 00 0 0 VX 573 0 13 0 0 0

In Table 8, for example, the composite object HB has 114,085 of revenue,5,654 open interest, and 4 broker marks. Using Table 7, the rank valuefor composite object HB is α+β+broker marks which equals 2+2+4=8.

Composite objects with rank values of zero may be excluded. In certainembodiments, a threshold rank value may be used to exclude low ratedcomposite objects. For example, in Table 8 above, the composite objectsIQ, NI, VH, VN, and VX may be excluded as these composite objects have azero rank value. A zero rank value may indicate that the compositeobjects have little to no interest or revenue (depending on the rankvalue calculations and inputs). As the composite objects have littleinterest or revenue, the exchange system is unable to verify that theprice or other data for the composite object is accurate. As such, theexchange system attempts to not use composite objects with low rankingvalues. After excluding low rated composites, the remaining subset ofcomposite objects may be use to generate paths (solutions) forcalculating values for the base objects.

At act A240, the exchange system generates paths between the baseobjects and a core object using a maximum spanning tree algorithm withthe generated rank values as the weights. A maximum spanning tree is aspanning tree of a weighted graph having maximum weight. A maximumspanning tree may be computed by using Kruskal Algorithm with edgessorted into decreasing order.

FIG. 10A and FIG. 10B depict a maximum spanning tree for the first subgroup of the graph of FIG. 9. Using the table 8 above and excluding thecomposite objects with a zero rank value, the composite objects aresorted from highest to lowest rank value. Using the highest rankedcomposite object, the system identifies the edge (composite object) inthe graph accordingly then adds the two related base objects to atemporary group. The highest scoring composite object is compositeobject 103 referred to in table 8 as HB. Composite object HB has as itslegs XF and XA. The composite object HB is shown in FIG. 10A.

As shown in FIG. 10B, additional composite objects are added to thegraph starting with the next highest ranked composite object with thelegs (base objects) being added to the temporary group. Compositeobjects with both legs (base objects) in the temporary group are notadded as this generates a closed loop. Proceeding down the table ofrankings, the next composite segments are added to the graph. Thesecomposite objects include (XK-XA), (FD, XA), (XA-XN), (XF-VJ), and (XM,XK) as shown in FIG. 10B. In the example, we will skip (XK-FD, shown asa dotted line 104) because both of the legs (XK and FD) are already inthe temporary group. Adding this composite object would create a closedloop of XK, FD, and XA.

FIG. 11 depicts an example maximum spanning graph 105 after adding thelast two composite objects that had a rank value greater than zero. Asshown there are two disparate groups. The first group 107 includes thecore object FD and base objects XK, XM, XA, XN, GH, XF, and VJ. Thesecond group 109 includes the base objects NX and VL.

Due to the low quality of the connections to core object FD, the secondgroup 109 NX and VL may not be priced from the core product FD. Thecomposite object NX-GH appears to be not actively traded or transacted.The veracity of the composite object NX-GH is not verified by externalsources. As such, the price of the composite object NX-GH may not beaccurate and should not be used to price other objects. NX and VL maypossibly be pricing used other methods or from a different core product.

As a comparison, the total revenue for the solutions/pricing routes forthe maximum spanning graph 105 may be compared to the simple pricingcomplex as described above in FIG. 5. The total open interest for allthe composite object in the simple pricing complex (FIG. 5) is 17,834.The total open interest for all the composite objects in the maximumspanning graph 105 is 34,380. The total revenue for all the compositeobject in the simple pricing complex is $447,290. The total revenue forall the composite objects in the maximum spanning graph 105 is $792,873.This comparison illustrates that maximum spanning graph 105 containscomposite objects with both more open interest and revenue. Theresulting solutions give more priorities to the composite objects havemore OI and composite objects that generate more revenue e.g. moreimportant composite objects/more liquid composite objects. A confidencevalue may be generated for each graph based on the total revenue or openinterest or other market data. The confidence value may be used forcomparison with previous confidence values or to evaluate the pricingsolutions.

In certain embodiment, the resulting paths are aggregated with otherpaths from other sub-groups. For example, the second sub-group that wasgenerated for the second core object TL.

FIG. 12A and FIG. 12B depict a graph of and the maximum spanning treefor the second sub-group 95 of FIG. 9.

As shown in FIG. 12A, the second sub-group 95 includes the base objectsXA, XB, RF, and TL and five composite objects (HB, FH, IP, IT andIX—described below in Table 9). Repeating steps A220-A240 for the secondsub-group 95 generates a maximum spanning graph as shown in FIG. 12B.FIG. 12B further shows the rank values for each composite object in thegraph.

The rank values for the composite objects may be calculated by using thefollowing received data and the scoring mechanism used for the first subgroup:

TABLE 9 #of Composite open broker Rank Object Leg 1 Leg 2 Revenueinterest marks value HB TL XB 64085 3654 4 6 FH TL XA 16302 1320 2 2 IPXB XA 105007 5354 3 7 IT XA RF 234613 8230 3 7 IX TL RF 92315 5020 3 6

From the maximum spanning tree the values for each of the base objectsmay be derived. For example, value for the base object XA may becalculated using the solution including the composite objects TL-RF andRF-XA. XA, however, also has a pricing path derived from the first subgroup. The value for XA may be derived from the composite objects FD-XNand XN-XA. As XA was priced in both subgroups the exchange system needsto determine which solution should be used. In certain embodiments, thesum of revenue of the composite objects in each of the pricing paths maybe divided by the number of composite objects to arrive at a value. Thesolution with the higher value may then be selected for use. For thisexample, the first sub-group's pricing solution for XA has a totalrevenue of 145,802. The sum divided by the number of composite objectsis 72,901. For the second sub-group's pricing solution for XA, the totalrevenue is 326,928. The sum divided by the number of composite objectsis 163,464. As such, the

At act A250, the exchange system calculates values for the one or moreobjects. As determined in the previous acts, the pricing solution foreach base object in the product complex will be:

XM: XK-XA-FD

XK: XA-FD

XA: RF-TL

XN: XA-FD

XF: XA-FD

VJ: XF-XA-FD

GH: FD

RF: TL

XB: RL

NX: cannot price

VL: cannot price

The values may be used to determine a mark to market amount on a dailybasis during the term of the financial instrument. Such determinationsmay be made on a settlement date for the financial instrument for thepurposes of final settlement.

Although some of the examples discussed herein relate to futurescontracts and associated spread instruments, the disclosed embodimentsfor the optimization module may be applicable to options contracts, andin particular, to strike prices options contracts. For example, eachoptions contract may include multiple strike prices, and an exchangesystem may receive multiple values for each strike price for an outrightoptions contract. Moreover, even after the settlement module processesthe received values, the exchange may have the choice of selecting oneof multiple values for the strike prices for the options contracts.Thus, the optimization module may convert or translate each strike pricefor each options contract into a base object. The system may alsoconvert spread instruments between strike prices into composite objects.

The illustrations of the embodiments described herein are intended toprovide a general understanding of the structure of the variousembodiments. The illustrations are not intended to serve as a completedescription of all of the elements and features of apparatus and systemsthat utilize the structures or methods described herein. Many otherembodiments may be apparent to those of skill in the art upon reviewingthe disclosure. Other embodiments may be utilized and derived from thedisclosure, such that structural and logical substitutions and changesmay be made without departing from the scope of the disclosure.Additionally, the illustrations are merely representational and may notbe drawn to scale. Certain proportions within the illustrations may beexaggerated, while other proportions may be minimized. Accordingly, thedisclosure and the figures are to be regarded as illustrative ratherthan restrictive.

While this specification contains many specifics, these should not beconstrued as limitations on the scope of the invention or of what may beclaimed, but rather as descriptions of features specific to particularembodiments of the invention. Certain features that are described inthis specification in the context of separate embodiments can also beimplemented in combination in a single embodiment. Conversely, variousfeatures that are described in the context of a single embodiment canalso be implemented in multiple embodiments separately or in anysuitable sub-combination. Moreover, although features may be describedabove as acting in certain combinations and even initially claimed assuch, one or more features from a claimed combination can in some casesbe excised from the combination, and the claimed combination may bedirected to a sub-combination or variation of a sub-combination.

Similarly, while operations are depicted in the drawings and describedherein in a particular order, this should not be understood as requiringthat such operations be performed in the particular order shown or insequential order, or that all illustrated operations be performed, toachieve desirable results. In certain circumstances, multitasking andparallel processing may be advantageous. Moreover, the separation ofvarious system components in the embodiments described above should notbe understood as requiring such separation in all embodiments, and itshould be understood that the described program components and systemscan generally be integrated together in a single software product orpackaged into multiple software products.

One or more embodiments of the disclosure may be referred to herein,individually and/or collectively, by the term “invention” merely forconvenience and without intending to voluntarily limit the scope of thisapplication to any particular invention or inventive concept. Moreover,although specific embodiments have been illustrated and describedherein, it should be appreciated that any subsequent arrangementdesigned to achieve the same or similar purpose may be substituted forthe specific embodiments shown. This disclosure is intended to cover anyand all subsequent adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent to those of skill in theart upon reviewing the description.

The Abstract of the Disclosure is provided to comply with 37 C.F.R. §1.72(b) and is submitted with the understanding that it will not be usedto interpret or limit the scope or meaning of the claims. In addition,in the foregoing Detailed Description, various features may be groupedtogether or described in a single embodiment for the purpose ofstreamlining the disclosure. This disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter may be directed toless than all of the features of any of the disclosed embodiments. Thus,the following claims are incorporated into the Detailed Description,with each claim standing on its own as defining separately claimedsubject matter.

It is therefore intended that the foregoing detailed description beregarded as illustrative rather than limiting, and that it be understoodthat it is the following claims, including all equivalents, that areintended to define the spirit and scope of this invention.

What is claimed is:
 1. A computer implemented method to generate asolution for determining a value for one or more base data objectsstored in a memory using data indicative of a level of activity and anumber of sources for each of a plurality of composite data objectsstored in the memory transacted by a transaction system processor, theplurality of composite data objects including the one or more base dataobjects, the method comprising: identifying, by a processor incommunication with the memory, from the set of base data objects, afirst core data object therein, the core data object including objectvalue data corroborated by a plurality of external sources; receiving,from the transaction system processor, the data indicative of the levelof activity and the number of sources for each of the plurality ofcomposite data objects, each of the plurality of composite data objectshaving an object relationship from the particular base data object tothe first core data object and having a value as a whole that is relatedto the value of the base data objects that comprise each of theplurality of composite data objects; generating, by the processor, aranking value indicative of a level of confidence in the data for eachof the plurality of composite data objects as a function of the dataindicative of the level of activity for each of the plurality ofcomposite data objects and the number of sources; generating, by theprocessor and storing in the memory, a weighted graph comprising theplurality of composite data objects and based on the objectrelationships thereof, the one or more base data objects, the first coredata object, and the ranking value of the each of the composite dataobjects; generating, by the processor and storing in the memory, a firstmaximum spanning tree of the weighted graph; and generating, by theprocessor and storing in the memory, from the first maximum spanningtree, a first subset of the plurality of composite data objects used todetermine the value of one or more base data objects.
 2. The computerimplemented method of claim 1, wherein the plurality of composite dataobjects comprise exchange offered spreads of the one or more baseobjects.
 3. The computer implemented method of claim 1, wherein the dataindicative of the level of activity comprises revenue data for each ofthe plurality of composite data objects.
 4. The computer implementedmethod of claim 1, wherein the data indicative of the level of activitycomprises open interest data for each of the plurality of composite dataobjects.
 5. The computer implemented method of claim 1, furthercomprising: excluding one or more composite data objects with a rankingvalue below a threshold value from the weighted graph.
 6. The computerimplemented method of claim 1, further comprising: generating, by theprocessor, a second weighted graph comprising the plurality of compositedata objects, the one or more base data objects, a second core dataobject, and the ranking value of the each of the composite data objects;generating, by the processor, a second maximum spanning tree of theweighted graph; aggregating, by the processor, the first maximumspanning tree with the second maximum spanning tree; and generating, bythe processor, from the aggregated maximum spanning tree, a secondsubset of the plurality of composite data objects used to determine thevalue of one or more base data objects.
 7. The computer implementedmethod of claim 6, wherein aggregating comprises: selecting a higherranked path of one or more paths in the first and second maximumspanning trees.
 8. The computer implemented method of claim 1, whereinderiving the ranking value comprises: calculating the ranking valueusing an equation α+β+broker marks, wherein α is a function of the dataindicative of the level of activity, wherein β is a function of thenumber of sources.
 9. The computer implemented method of claim 1,further comprising: generating a confidence value for the first maximumspanning tree.
 10. The computer implemented method of claim 1, furthercomprising: receiving, by the processor, updated data indicative of alevel of activity for each of the plurality of composite data objects;and scoring, by the processor, each of the plurality of composite dataobjects as a function of the updated data indicative of the level ofactivity.
 11. A computer system configured to generate a solution fordetermining a value for one or more base data objects store in a memoryusing data indicative of a level of activity and a number of sources foreach of a plurality of composite data objects stored in the memory andtransacted by a transaction system processor, the plurality of compositedata objects including the one or more base data objects, the computersystem comprising: an identification module configured to identify fromthe set of base data objects, a first core data object therein, the coredata object including object value data corroborated by a plurality ofexternal sources; a receiver configured to receive, from the transactionsystem processor, the data indicative of the level of activity and thenumber of sources for each of the plurality of composite data objects,each of the plurality of composite data objects having an objectrelationship from the particular base data object to the first core dataobject and having a value as a whole that is related to the value of thebase data objects that comprise each of the plurality of composite dataobjects; an evaluator configured to generate a ranking value indicativeof a level of confidence in the data for each of the plurality ofcomposite data objects as a function of the data indicative of the levelof activity for each of the plurality of composite data objects and thenumber of sources; the evaluator further configured to generate andstore in the memory a weighted graph comprising the plurality ofcomposite data objects and based on the object relationships thereof,the one or more base data objects, the first core data object, and theranking value of the each of the composite data objects; the evaluatorfurther configured to generate and store in the memory a first maximumspanning tree of the weighted graph and generate from the first maximumspanning tree, a first subset of the plurality of composite data objectsused to determine the value of one or more base data objects.
 12. Thecomputer system of claim 11, wherein the data indicative of the level ofactivity comprises revenue data for each of the plurality of compositedata objects.
 13. The computer system of claim 11, wherein the dataindicative of the level of activity comprises open interest data foreach of the plurality of composite data objects.
 14. The computer systemof claim 11, wherein the plurality of composite data objects compriseexchange offered spreads of the one or more base objects.
 15. Thecomputer system of claim 11, wherein the evaluator is further configuredto exclude one or more composite data objects with a ranking value belowa threshold value from the weighted graph.
 16. The computer system ofclaim 11, wherein the evaluator is further configured to: generate asecond weighted graph comprising the plurality of composite dataobjects, the one or more base data objects, a second core data object,and the ranking value of the each of the composite data objects;generate a second maximum spanning tree of the weighted graph; aggregatethe first maximum spanning tree with the second maximum spanning tree;and generate from the aggregated maximum spanning tree, a second subsetof the plurality of composite data objects used to determine the valueof one or more base data objects.
 17. The computer system of claim 11,wherein the evaluator, to generate the ranking value, is furtherconfigured to calculate the ranking value using an equation α+β+brokermarks, wherein α is a function of the data indicative of the level ofactivity, wherein β is a function of the number of sources.
 18. Thecomputer system of claim 11, wherein the evaluator is further configuredto generate a confidence value for the first maximum spanning tree. 19.The computer system of claim 11, wherein the receiver is furtherconfigured to: receive updated data indicative of a level of activityfor each of the plurality of composite data objects; and score each ofthe plurality of composite data objects as a function of the updateddata indicative of the level of activity.
 20. A system configured togenerate a solution for determining a value for one or more base dataobjects stored in a memory using data indicative of a level of activityand a number of sources for each of a plurality of composite dataobjects stored in the memory transacted by a transaction systemprocessor, the plurality of composite data objects including the one ormore base data objects, the method comprising: means for identifyingfrom the set of base data objects, a first core data object therein, thecore data object including object value data corroborated by a pluralityof external sources; means for receiving, from the transaction system,the data indicative of the level of activity and the number of sourcesfor each of the plurality of composite data objects, each of theplurality of composite data objects having an object relationship fromthe particular base data object to the first core data object and havinga value as a whole that is related to the value of the base data objectsthat comprise each of the plurality of composite data objects; means forgenerating a ranking value indicative of a level of confidence in thedata for each of the plurality of composite data objects as a functionof the data indicative of the level of activity for each of theplurality of composite data objects and the number of sources; meansgenerating and storing in the memory, a weighted graph comprising theplurality of composite data objects and based on the objectrelationships thereof, the one or more base data objects, the first coredata object, and the ranking value of the each of the composite dataobjects; means for generating and storing in the memory, a first maximumspanning tree of the weighted graph; and means for generating andstoring in the memory, from the first maximum spanning tree, a firstsubset of the plurality of composite data objects used to determine thevalue of one or more base data objects.